The Role of Big Data Analytics in Innovation: A Study from the Telecom Industry

Posted: March 27th, 2020

The Role of Big Data Analytics in Innovation: A Study from the Telecom Industry

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Table of Contents

Introduction. 3

Critical Review.. 3

Additional References. 6

Literature Gap. 10

Conclusions. 11

References. 13

Introduction

            Technological advancements have led to use of big data analytics to increase knowledge, innovation, and competitive advantage for modern organizations. Big data analytics within the telecom industry has brought about significant changes in the way that businesses source, collect and analyze information (Watson 2014). With a diverse set of data, these organizations can examine and solve their problems creatively and efficiently. The innovation also allows them to attain cost-effective strategies of business operations, thus helping them save financial resources in the process (LaValle et al. 2012; Chen et al. 2012). Furthermore, the phenomenon has rendered traditional forms of data collection almost obsolete.

            The comprehensive analysis deals with the report, “The Role of Big Data Analytics in Innovation: A Study from the Telecom Industry.” Here, the authors make a careful evaluation of how the telecommunications industry has enhanced its innovative techniques using big data. The objective of this report is to provide an interpretative analysis of the resource and evaluate various research and literature gaps in the field of big data analytics as applied in the telecom industry. The report also covers some of the information sources provided within this article to give a comprehensive assessment of the subject.

Critical Review

The report under review deals with the manner in which the collection of massive sets of data has aided businesses to resolve serious predicaments. It also discusses the way in which organizations analyze and manage data to develop highly innovative strategies that provide a competitive advantage. The purpose of the report was, therefore, to examine how telecom companies collect and process big data to offer these innovative solutions. This qualitative study uses both primary and secondary information from three companies in Jordan. Some of the primary data sources include GPS, mobile usage data, interviews, marketing survey, and reviews and comments provided to the organizations.

The research has the objective of answering the question of how impactful big data analytics has been in telecom companies regarding their added capacities. Additionally, the study investigates how absorptive capacity helps to improve innovation. According to Al-Jaafreh and Fayoumi (2017), contemporary organizations survive and thrive through the adoption of innovative strategies, which they gain from both external and internal knowledge. Previous studies have explored the basic concepts of big data analytics, such as the types of techniques used to gather data. Researchers have also investigated other technical aspects of big data, such as methods of analysis and management. Other commonly explored areas include examining active software systems used to analyze big data, whether qualitative or quantitative. However, the authors note that an existing gap within this field of study is how organizations apply this information to enhance innovation within its working environment. In addition, the report does not explore the business value of big data sufficiently.

In light of this, the authors also note that extensive studies have drawn a connection between information and innovation within the organization. Nonetheless, it remains unclear how these elements interact, as well as the additional resources required to achieve their innovation goals. Al-Jaafreh and Fayoumi (2017) consequently assert, “To create BDA capability, organizations should focus on many resources such as infrastructure and people” (pg. 3). A critical conceptual aspect of the study is an organization’s absorptive capacity. The concept is the ability of a firm to gain, adapt, transform, and implement knowledge to generate a more effective and dynamic function. The firm’s absorptive capacity has a direct association to its ability to apply the knowledge that big data provides once it has been collected and analyzed. Furthermore, the notion relates to the firm’s ability to store and manage large volumes of data. Therefore, the success of enhancing innovation from big data analytics is dependent on not only the big data but also on the availability of other resources as well as the effectiveness of their interactions.

The concept of absorptive capacity has its roots in the resource-based theory. This theory provides a managerial structure that incorporates the organization’s diverse strategic resources as a means of gaining a sustainable advantage in today’s competitive markets. Within the Jordanian telecom companies in the study, the resource-based theory is relevant to study how these businesses can use human resources, technological infrastructure, money, and big data to gain innovation, which is the value that bolsters their market positions.

Al-Jaafreh and Fayoumi (2017) maintain that the overall significance of the study is to provide an in-depth analysis of how big data interacts with other resources in the firm to add value. The information allows managers to enhance their practice based on the findings. The interface also provides them with more control over how large volumes of data are handled, understanding their worth in the innovative processes. One limitation of the data is that it is limited to telecom companies in one country. While the study is performed using a small population, the concepts of absorptive capacity can be applied across all professional fields.

A vital aspect of the study is that it possesses a strong background in big data analytics. Many of the resources used are recent, peer-reviewed, and relevant to the research. The sources are from experts, and this has enhanced their reliability as far as research on the concept is concerned. Due to the proficiency exhibited by the authors, the journal articles also manage to integrate other distinct areas with the notion of big data analytics. These information sources provide a robust context that the reader can use to draw the connection between innovation and data analytics. However, since the specific gap of study has not been sufficiently investigated in the past, information on the subject is limited.

Additional References

Reference Summary Relevance
Akter, S, Wamba, SF, Gunasekaran, A, Dubey, R & Childe, SJ 2016, ‘How to improve firm performance using big data analytics capability and business strategy alignment,’ International Journal of Production Economics, vol. 182, pp.113-131.   The article focuses on the growing interest in big data analytics. As companies continue to embrace big data analytics and developing such capabilities, it becomes essential to evaluate how it affects performance. The main problem presented is that the technique cannot be applied for all organizations despite its successes in several sett workings. The resource-based theory has been used to develop a Big Data Analytics Capability (BDAC) model that provides critical insight into its implementation. From the study carried out, the findings reveal that the BDAC model is composed of three categories including management, technology, and talent capability. Within those criteria, aspects that include preparation, management, consistency, technology administration familiarity, harmonization, investment, relational awareness, and business comprehension were critical to the accomplishment of the model. The study also revealed the intricate and multidimensional aspect of big data analytics, especially when interacted with other organizational resources. The article is relevant to the study because it provides a strong background regarding the concept of absorptive capacity that a firm needs to have to apply big data for innovation. The articles examine the relationship between big data analytics and organizational performance. Therefore, it provides a foundation for the study of big data and its benefits through innovative strategies. A resource-based approach is used to create the BDAC model, which allows us to examine how resources interact with big data to improve performance. The study can provide more evidence that supports the authors’ position that big data can only work when associated with other resources.
Côrte-Real, N, Oliveira, T & Ruivo, P 2017, ‘Assessing business value of big data analytics in European firms,’ Journal of Business Research, vol. 70, pp. 379-390.   European firms have developed strategic technological skills and capabilities to gain a competitive advantage in today’s markets. According to the authors, dynamic capabilities are essential for strengthening the position of a company. In light of this assertion, there has been increasing demand for big data analytics as it has provided firms with competitive business value. From the study of 500 European firms, the authors’ findings were that big data analytics could enhance the overall agility of an organization. The supplement is achieved through the management of knowledge, infrastructure, and other resources. Furthermore, Corte- Real and colleagues (2017) reveal that agility may “partially mediate the effect between knowledge assets and performance” (pg. 5). As such, the article provides an excellent background that explains the relationship between the business value from big data analytics and competitive advantage. The article has been beneficial to the study because it provides a theoretical background concerning the value of big data to large organizations. The study also covers resource management through an analysis of dynamic capabilities. An essential value that big data analytics provides to these firms is the enhancement of adaptive abilities. Adaptation is a valuable asset that growing firms need to possess to thrive in a competitive industry.
Kambatla, K, Kollias, G, Kumar, V & Grama, A 2014, ‘Trends in big data analytics. Journal of Parallel and Distributed Computing,’ vol. 74, no. 7, pp. 2561-2573.   The resource provides an assessment of big data analytics. According to Kambatla and colleagues (2014), an increasing interest in big data analytics has been attributed to the wide range of potential benefits it provides to achieve organizational success. The authors focus on the present and future trends of big data, examining how applicable it has been towards organizational development. The article also discusses both hardware and software resources and methods that can be used to analyze and implement big data. Hardware and software tools contribute to the resources needed to achieve a desirable level of absorptive capacity. Therefore, the article provides relevant background to the study because it allows us to examine how these tools, in particular, can be utilized and interacted with big data as well as other required resources. Hardware tools in big data provide the infrastructure used to collect and store large volumes of data. Software tools facilitate management and analysis.

Literature Gap

The study focused on how big data analytics has benefited organizations within the telecoms industry. One significant gap is that it has focused on only three organizations in Jordan. This small sample size presents limitations in its capacity to be representative of all telecom industries on a global scale. Furthermore, Jordan is a country whose people have different cultural values and practices from populations living in Sub-Saharan Africa, Europe, Asia, and America. The discrepancy will affect how data may be collected and utilized for the organization’s competitive advantage. For instance, different organizations develop disparate perceptions regarding the concept of organizational value. The diversity in perspective will also affect the type of data collected as well as techniques used to achieve innovative strategies. Therefore, future studies may focus on gaining more data for telecom companies outside the country, and across other parts of the world.

The study was aimed at improving management practices using big data analytics and innovation. However, the resource did not provide a sufficient background into how organizational culture affects management, and how big data is utilized. According to Thirathon and colleagues (2017), decision making on a managerial level is influenced by cultural factors such as their analytic cultures and practice. Their views will ultimately shape this practice based on their target market and the broader societies in which they operate. Similarly, Chhokar and colleagues (2013) focus on different management styles across several cultures in the world. The difference in management will determine how leaders perceive tools and resources that benefit their organizations. Therefore, implementation of big data will be highly dependent on this factor. The research question that can be used as a foundation for future studies is: “How does organizational culture affect the manner in which big data analytics is carried out?”

Another literature gap is the examination of the relationship between big data analytics and innovativeness across all industries. The resource focused on the use of big data in telecom companies alone. As such, additional considerations for other organizations may be overlooked. For instance, the perspective of innovation and business value within a telecom company will differ significantly from that of a retail, food, or fashion company. If big data analytics is used to evaluate customer needs and preferences, the study of one industry alone will not provide sufficient frameworks of the market behavior, which can be used to predict future outcomes. Future studies may focus on collecting data across different sectors and develop common patterns of how big data analytics influences innovation.

Conclusions

Technological advancements in today’s organizations have been characterized by the collection, analysis, and utilization of large volumes of information referred to as big data. Through this, organizations have been able to generate more revenue, reduce operational costs, and achieve competitive advantages. The article focused on how big data analytics has been used to enhance innovative strategies within telecom companies. Three companies in Jordan were selected for this study. Results showed that telecom companies have been able to benefit from their absorptive capacity, which involves connecting big data with other resources to generate beneficial information. The additional sources of information provide sufficient background regarding the study of big data analytics, which provides a foundation for the study itself. Gaps in the literature include the study of a small sample of organizations (only three companies) which is not representative on a global scale. Another shortcoming is that cultural factors were not considered because the managerial culture in Jordan differs from the practices exercised in other regions. Lastly, the study is affected by the localized nature of its research on big data analytics and innovation. The research only focused on the telecom industry and drew conclusions that only apply to the respective sector. However, the implementation of these findings may be incapacitated due to the diverse nature of industries. Future studies should focus on broadening the scope of research to include more industries.

References

Akter, S, Wamba, SF, Gunasekaran, A, Dubey, R & Childe, SJ 2016, ‘How to improve firm performance using big data analytics capability and business strategy alignment,’ International Journal of Production Economics, vol. 182, pp.113-131.

Al-Jaafreh, A & Fayoumi, A 2017, ‘The role of big data analytics in innovation: A study from the telecom industry,’ ACIS.

Chen, H, Chiang, RH & Storey, VC 2012, ‘Business intelligence and analytics: From big data to big impact,’ MIS Quarterly, pp.1165-1188.

Chhokar, JS, Brodbeck, FC & House, RJ (eds) 2013, Culture and leadership across the world: The GLOBE book of in-depth studies of 25 societies, Routledge.

Côrte-Real, N, Oliveira, T & Ruivo, P 2017, ‘Assessing business value of big data analytics in European firms,’ Journal of Business Research, vol. 70, pp. 379-390.

Kambatla, K, Kollias, G, Kumar, V & Grama, A 2014, ‘Trends in big data analytics. Journal of Parallel and Distributed Computing,’ vol. 74, no. 7, pp. 2561-2573.

Kwon, O, Lee, N & Shin, B 2014, ‘Data quality management, data usage experience and acquisition intention of big data analytics,’ International Journal of Information Management, vol. 34, no. 3, pp.387-394.

LaValle, S, Lesser, E, Shockley, R, Hopkins, MS & Kruschwitz, N, 2011, ‘Big data, analytics and the path from insights to value,’ MIT Sloan Management Review, vol. 52, no. 2, p.21.

Thirathon, U, Wieder, B, Matolcsy, Z & Ossimitz, ML 2017, ‘Big data, analytic culture and analytic-based decision-making evidence from Australia,’ Procedia Computer Science, vol. 121, pp.775-783.

Watson, HJ, 2014, ‘Tutorial: Big data analytics: Concepts, technologies, and applications,’ CAIS, vol. 34, p. 65.

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